[sarusso]

The example paper does not mention what type of diabetes it is about - if type 1 or type 2 - and they have very different risk factors.

While it’s kind of clear form the context that it’s about type 2, I doubt a paper like this would pass a peer review without stating it explicitly, in particular with respect to the data set that could potentially include both. Rigor is essential in drawing scientific conclusions.

I guess this is a good example about the statistical nature of LLMs outputs (type 2 is the most common) and consequentially their limitations...

[twobitshifter]

Can we feed llms peer reviews and add a reviewer stage to this? Multi agent system would likely catch the poor effort submissions. It could either just reject or provide feedback if the recommendation was to revise.

[ttaallooss]

The hypothesis you have raised about the source of the implicit assumptions these models make is indeed an interesting and plausible one, in my opinion.

Biases in data will always exist, as this is the nature of our world. We need to think about them carefully and understand the challenges they introduce, especially when training large "foundational" models that encode a vast amount of data about the world. We should be particularly cautious when interpreting their outputs and when using them to draw any kind of scientific conclusions.

I think this is one of many reasons why we implemented the system with inherent human overseeing and strongly encourage people to provide input and feedback throughout the process.